Summary of A Proximal Policy Optimization Based Intelligent Home Solar Management, by Kode Creer et al.
A proximal policy optimization based intelligent home solar management
by Kode Creer, Imitiaz Parvez
First submitted to arxiv on: 5 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework, based on Proximal Policy Optimization (PPO), uses recurrent rewards to maximize the profits of prosumers under dynamic electricity markets. The PPO algorithm effectively models rewards to accumulate over 30% more total profits compared to naive algorithms. This demonstrates the potential for reinforcement learning in complex domains like financial markets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A smart grid allows people to sell extra electricity back to the power company, as long as they have solar panels or wind turbines and storage units. To make the most money from this process, a framework is needed that can plan ahead. The proposed framework uses an algorithm called Proximal Policy Optimization (PPO) to decide what actions to take based on how much money it can make in the future. This approach worked better than other methods, showing promise for using machine learning to solve complex problems like managing financial markets. |
Keywords
* Artificial intelligence * Machine learning * Optimization * Reinforcement learning
